An empirical survey on long document summarization: Datasets, models, and metrics
Long documents such as academic articles and business reports have been the standard
format to detail out important issues and complicated subjects that require extra attention. An …
format to detail out important issues and complicated subjects that require extra attention. An …
A survey on multi-modal summarization
The new era of technology has brought us to the point where it is convenient for people to
share their opinions over an abundance of platforms. These platforms have a provision for …
share their opinions over an abundance of platforms. These platforms have a provision for …
Exploring the efficacy of automatically generated counterfactuals for sentiment analysis
While state-of-the-art NLP models have been achieving the excellent performance of a wide
range of tasks in recent years, important questions are being raised about their robustness …
range of tasks in recent years, important questions are being raised about their robustness …
Trillion dollar words: A new financial dataset, task & market analysis
Monetary policy pronouncements by Federal Open Market Committee (FOMC) are a major
driver of financial market returns. We construct the largest tokenized and annotated dataset …
driver of financial market returns. We construct the largest tokenized and annotated dataset …
A rationale-centric framework for human-in-the-loop machine learning
We present a novel rationale-centric framework with human-in-the-loop--Rationales-centric
Double-robustness Learning (RDL)--to boost model out-of-distribution performance in few …
Double-robustness Learning (RDL)--to boost model out-of-distribution performance in few …
Numhtml: Numeric-oriented hierarchical transformer model for multi-task financial forecasting
Financial forecasting has been an important and active area of machine learning research
because of the challenges it presents and the potential rewards that even minor …
because of the challenges it presents and the potential rewards that even minor …
A survey of large language models in finance (finllms)
Large Language Models (LLMs) have shown remarkable capabilities across a wide variety
of Natural Language Processing (NLP) tasks and have attracted attention from multiple …
of Natural Language Processing (NLP) tasks and have attracted attention from multiple …
Combining intra-risk and contagion risk for enterprise bankruptcy prediction using graph neural networks
Predicting the bankruptcy risk of small and medium-sized enterprises (SMEs) is crucial for
making decisions about loans. Existing studies in both finance and AI research fields …
making decisions about loans. Existing studies in both finance and AI research fields …
A Survey on Large Language Models for Critical Societal Domains: Finance, Healthcare, and Law
In the fast-evolving domain of artificial intelligence, large language models (LLMs) such as
GPT-3 and GPT-4 are revolutionizing the landscapes of finance, healthcare, and law …
GPT-3 and GPT-4 are revolutionizing the landscapes of finance, healthcare, and law …
Predicting financial distress using multimodal data: An attentive and regularized deep learning method
The proliferation of multimodal data provides a valuable repository of information for
financial distress prediction. However, the use of multimodal data faces critical challenges …
financial distress prediction. However, the use of multimodal data faces critical challenges …